While of course this is not an exhaustive list of all the necessary next steps, it already provides an interesting set of challenges that should be addressed.
Note that while we are advocating for Explainable Planning as a service, we are well aware that this is not the only way to provide explanations for planning. In particular, we envisage at least the following possible limitations that nevertheless represent important research questions that should be considered by the Explainable Planning community.
First we acknowledge that contrastive explanations are not suitable to answer every type of question that the user might have. However, we argue that contrastive questions are common and that contrastive explanations therefore play a significant role, as also acknowledged by other researchers in Explainable AI, e.g, [1]}.
Second, by lifting the requirement that the explanation is generated by the planner used to generate the original plan, it could be possible to modify the search procedure used by the planner to generate explanations from a wider set of constraints.
Third, we are assuming that there is no uncertainty in the original planning model and that the model is correct. However, this is not always the case and for this, the body of research on model reconciliation plays a very important role [2]}.
